Approaches to imitation learning in robotics have delivered huge success ranging from helicopter acrobatics [1]}, high-speed arm skills [2]}, haptic control [3]}, [4]}, gestures [5]},
manipulation [6]}, [7]}, [8]} to legged locomotion [9]}, [8]}.
The machine learning algorithms that make imitation learning possible are well studied and have recently been summarized [11]}. Surprisingly, despite all of these impressive successes in the acquisition of new robot motor skills, fundamental research questions in imitation learning of central importance have remained open for decades.
Among such core questions is the correspondence problem: how can one agent (the learner or imitator) produce a similar behavior - in some aspect - to behavior it perceives in another agent (the expert or demonstrator), given that the two agents obey different kinematics and dynamics (body morphology, degrees of freedom (DOFs), constraints, joints and actuators, torque limits), i.e., occupy different state spaces [12]}?
